Friday, March 21, 2025

After Chatbots, Embodied AI (Robots, Vehicles, Devices, Machines)

Right now, the typical casual observer might tend to think of generative artificial intelligence as an AI chatbot or AI assistant. But physically-embodied  AI is going to be a big part of  next wave of growth for generative AI


Artificial intelligence increasingly is going to be “embodied” in devices and machines such as vehicles and robots, illustrating the way AI now is reshaping older markets such as the “internet of things.” Researchers at Goldman Sachs, for example, have estimated that the market for humanoid robots could reach $38 billion by 2035. 


Within industrial robotics, different form factors address different workloads. Robotic arms are used for tasks such as picking, packaging, material handling and assembly.


Mobile robots typically run on wheels and are commonly used for moving inventory from one place to another inside industrial facilities.


And then we are also seeing a rise in humanoid robots, which more closely resemble the structure of human bodies, enabling them to complete tasks and move around just like us.


Among the firms looking to lead in this market are Tesla, Boston Dynamics, Figure AI, Apptronik and Agility Robotics. And then there are all the other firms that will try to create leading roles in support and management, such as AWS


Amazon got into robotics in 2012 when it created the ‘Amazon Robotics’ arm by acquiring a company called Kiva Systems, which specialized in warehouse technology. Between 2021 and 2024, Amazon expanded the number of robots deployed in its own operations from 350,000 to 750,000. 


The point is that AI, IoT, robotics, autonomous vehicles and embodied AI are starting to overlap. Looking only at the “Magnificent seven” firms, for example, embodied AI is enmeshed with firm earnings. 

source: Seeking Alpha



Company

Robotics

Autonomous Vehicles

Generative AI

Language Models

AI as a Service

Smartphone Apps

Apple

Yes, acquisition of Robotics firm

Yes, investment in AV startup, Voyage

Yes, Core ML

Yes, Siri

No

Yes, Core ML apps

Amazon

Yes, acquisition of Kiva Systems, Robotics firm

Yes, investment in AV startup, Aurora

Yes, SageMaker

Yes, Alexa

Yes, SageMaker

Yes, Alexa apps

Alphabet

Yes, Boston Dynamics, Robotics firm

Yes, Waymo, AV subsidiary

Yes, Google AI

Yes, Google Assistant

Yes, Google Cloud AI

Yes, Google Assistant apps

Facebook

No

Yes, investment in AV startup, Cruise

Yes, FAIR

Yes, Portal

Yes, Facebook AI

Yes, Portal apps

Microsoft

Yes, Azure Machine Learning

Yes, investment in AV startup, Cruise

Yes, Azure Machine Learning

Yes, Bing

Yes, Azure Machine Learning

Yes, Bing  apps

NVIDIA

Yes, Isaac, Robotics platform

Yes, Drive, AV platform

Yes, Deep Learning

No

Yes, NVIDIA AI

Yes, Deep Learning apps

Tesla

Yes, Autopilot, Robotics platform

Yes, Full Self-Driving

Yes, Autopilot AI

No

No

Yes, Autopilot apps


So embodied AI (robots, vehicles, machines, devices) are going to be important AI products, beyond today’s AI assistants or chatbots.


Good Outcomes Beat Good Intentions: How Dumb Are We?

Good intentions clearly are not enough when designing policies to improve home broadband availability in underserved areas. In fact, since 2021, more than three years after its passage, the U.S. Broadband Equity, Access, and Deployment (BEAD) program has yet to install a single new connection.  


It seems we were determined to make the perfect the enemy of the good, preventing construction until we mostly were certain our maps were accurate. A rival approach would have proceeded on the assumption that residents and service providers pretty much know where they have facilities and where they do not; where an upgrade can be conducted fast and easily, and where it cannot. 


And perhaps (despite the clear industry participant interests that always seem to influence our decisions) we should not have insisted on the “fastest speed” platforms. Maybe we’d have prioritized “good enough” connections that could be supplied really fast and enabled the outcomes we were looking for (getting the unconnected connected; getting the underserved facilities that do not impede their use of internet apps). 


This is not, to use the phrase, “rocket science.” We have known for many decades that “good enough” home broadband can be supplied fast, and affordably, if we use satellite (geostationary or low earth orbit, but particularly now LEO) or wireless to enable the connections. 


To those who say we need to supply fiber to the home, some of us might argue the evidence suggests relatively-lower speed (such as 100 Mbps downstream) connections supply all the measurable upside we seek, for homework, shopping, telework. The touted gigabit-per-second or multi-gigabit-per-second connections are fine, but there is very little evidence consumers can even use that much bandwidth. 


Study/Source

Key Findings

Distinguishing Bandwidth and Latency in Households' Willingness to Pay for Broadband Internet Speed (2017)

Consumers value increasing bandwidth from 10 to 25 Mbps at about $24 per month, but the additional value of increasing from 100 Mbps to 1 Gbps is only $19. This suggests diminishing returns for speeds beyond 100 Mbps.

Are you overpaying for internet speeds you don't need? (2025)

Research indicates that many Australians are overspending on high-speed internet connections they don't need. Most households can manage well with a 50 Mbps plan unless they engage in high-bandwidth tasks like 4K streaming or online gaming.

Simple broadband mistake costing 9.5 million households up to £113 extra a year (2024)

Millions of UK households are overpaying for broadband by purchasing higher speeds than necessary. Smaller households often need speeds up to 15 Mbps but pay for over 150 Mbps, wasting £113 annually.

ITIF (2023)

- US broadband speeds outpace everyday demands

- Only 9.1% of households choose to adopt 250/25 Mbps speeds when available

- Clear inflection point past 100 Mbps where consumers no longer see value in higher speeds

ITIF (2020)

- Average existing connections comfortably handle more than typical applications require

- A household with 5 people streaming 4K video simultaneously only needs 2/3 of current average tested speed

- Research shows reaching a critical threshold of basic broadband penetration is more important for economic growth than faster speeds

European Research (2020)

- Full fiber networks are not worth the costs

- Partial, not full end-to-end fiber-based broadband coverage entails the largest net benefits

US Broadband Data Analysis

- Compared to normal-speed broadband, faster broadband did not generate greater positive effects on employment

OpenVault Q3 2024 Report

- Average US household uses 564 Mbps downstream and 31 Mbps upstream

- Speeds around 500 Mbps sufficient for most families

FCC Guidelines

- 100-500 Mbps is enough for 1-2 people to run videoconferencing, streaming, and online gaming simultaneously

- 500-1000 Mbps suitable for 3 or more people with high bandwidth needs


We might all agree that, where it is feasible, fiber to home makes the most long-term sense. But we might also agree that where we want useful home broadband speeds, right now, everywhere, with performance that enables remote work, homework, online shopping and all other internet apps, then any platform delivering 100 Mbps (more for multi-user households, but likely not more than 500 Mbps even in the most-challenging use cases) will do the job, right now. 


Good intentions really are not enough. Good outcomes are what we seek. And that often means designing programs that we can implement fast, at lower cost, with wider impact, immediately or nearly so. “Better” platforms that cost more and are not built are hardly better.


Claude Adds Web Search Feature: What it Means

Claude now has added web search for  all paid Claude users in the United States, while support for users on free plans and in more countries is coming soon.


For Claude AI engine’s users, the new feature means up-to-date information beyond its training cutoff is added. So Claude should be more useful for real-time information needs. Users will not see messages that the provided information is only available through the end of 2023, for example, as is common for any engine without real-time search. 


Comparison: Search Engines vs. AI Assistants With and Without Web Search

Capability

Traditional Search Engines

AI Assistants with Web Search

AI Assistants without Web Search

Information Retrieval

Comprehensive access to indexed web content

Access to recent information beyond training data

Limited to information available in training data

Information Recency

Real-time updates and fresh content

Near real-time information (depending on search integration)

Limited by knowledge cutoff date

Result Presentation

List of relevant links requiring user navigation

Synthesized information with cited sources

Synthesized information from training data only

Complex Queries

Keyword-based with some natural language support

Natural language understanding with contextual web results

Natural language understanding limited to training knowledge

Source Transparency

Direct links to original sources

Can cite sources when providing information

Cannot reliably cite specific sources

Factual Accuracy

Varies by source quality; user must evaluate

Generally improved with access to current information

May provide outdated information or hallucinate when uncertain

Personalization

Based on search history and user data

Conversation context + web results tailored to query

Conversation context only

Multi-turn Interaction

Limited (requires new searches)

Strong conversational memory with updated information

Strong conversational memory with static knowledge base

Content Creation

Limited to search result presentation

Creative content informed by latest trends and data

Creative content limited to training data knowledge

Specialized Knowledge

Excellent for finding niche information

Can discover and explain specialized current topics

May struggle with niche or recent specialized topics

Exploration Breadth

Excellent for discovering diverse viewpoints

Can access diverse viewpoints but may synthesize

Limited to viewpoints represented in training data

User Effort Required

Higher (must sift through results)

Lower (provides direct answers with sources)

Lower (provides direct answers)


So Claude becomes more directly competitive with other AI assistants such as ChatGPT and Google's Gemini that already have search capabilities, at least in the paid versions for ChatGPT. It already is t he case that most of the AI assistants, at least in their paid versions, support search, so that feature is becoming table stakes.


Still, some will argue that Claude (as arguably is the case for many other AI assistants) is unlikely to fully substitute for traditional search engines. Search engines arguably remain better for broad exploration and discovery, while AI assistants are better for focused questions.


Search engines provide comprehensive results lists users can browse, while Claude provides synthesized information (even if some AI assistants provide the equivalent of footnotes showing where they got the information. So there are use cases where source attribution is important, and search engines might still be preferable sources.  


Still, adding web search should improve answers about current events with greater accuracy. The likelihood of hallucinations should also be reduced.


Thursday, March 20, 2025

"AI Edge Computing" is Multiple Markets, Not One

AI edge computing refers to the deployment of artificial intelligence algorithms at the "edge" of a network, closer to where data is generated, rather than relying solely on centralized cloud infrastructure. 


But there is a huge difference between “on-device” and “at a remote site” implementations, value chains and markets. 


For example,, on-device edge AI is about smartphones, IoT sensors, wearables or autonomous vehicles. 


Remote edge AI is about data centers, cloud computing, servers and other “enterprise” or “business” computing functions. 


Lumping everything together in one big “AI edge computing” category obscures as much as it illuminates. 


Category

Metric

On-Device Edge AI

Remote Edge AI

Source/Assumption

Market Size (2025)

Financial (USD Billion)

$15 billion

$10 billion

Based on edge AI market growth (e.g., Grand View Research, 21.7% CAGR from $20.78B in 2024)

Market Size (2030)

Financial (USD Billion)

$50 billion

$35 billion

Extrapolated from "device" and "data center" forecasts

Usage (2025)

Devices/Deployments

20 billion devices (smartphones, wearables)

500,000 edge nodes (e.g., servers, gateways)

Statista IoT, IDC edge spending forecasts

Compute Cycles (2025)

Avg. Cycles per Task

10^6 cycles (lightweight models, e.g., NLP)

10^9 cycles (complex models, e.g., video analytics)

Hardware capability estimates

Financial Implication

Revenue Driver

Hardware sales (AI chips, $500 billion smartphone market)

Infrastructure and services , perhaps $450 billion per year

On-device chips, smartphones for "on-device" mkt., connectivity and servers for "remote"

Growth Rate (2025-2030)

CAGR

27%

23%

Higher consumer device adoption vs. enterprise


As you can see, “edge AI” markets are largely contained within other existing device and data center markets. Looking at chip content alone, in either edge device or data center markets is helpful, but doesn’t show the full value chain for either type of product.


Does Claude 3.5 Haiku "Plan Ahead?"

Researchers looking at Antropic’s Claude language model 3.5 Haiku find evidence that Claude sometimes thinks in a conceptual space that is s...